INTERVIEW GUIDE

Amazon Data Engineer Interview: Questions & Process

Amazon's data engineer interview is half technical, half Leadership Principles. You'll write SQL, design data models and pipelines, and reason about warehousing — but every round also probes a Leadership Principle with behavioral questions answered in STAR format. Expect an online assessment or phone screen, then a 4-5 interview loop that includes a Bar Raiser whose job is to keep the bar high.

The interview process

1. Recruiter screen / online assessment — 30 min call and/or a timed SQL + scenario assessment

Tests: background, SQL fundamentals, and a first pass on Leadership Principles fit

2. Technical phone screen — 45-60 min

Tests: live SQL (joins, aggregation, window functions), data modeling basics, and one or two LP behavioral questions

3. Data modeling & warehousing round — 60 min

Tests: designing a star schema, slowly changing dimensions, fact vs dimension tables, and normalization tradeoffs

4. ETL / pipeline design round — 60 min

Tests: designing a batch or streaming pipeline, handling late/duplicate data, partitioning, and failure recovery

5. Bar Raiser + behavioral — 60 min

Tests: deep STAR dives on Leadership Principles — Ownership, Dive Deep, Deliver Results — led by a trained Bar Raiser outside the hiring team

Questions you're likely to get

Technical

Write a query to find the second-highest salary in each department.
Given orders and customers tables, compute each customer's first purchase date and 30-day retention.
Use a window function to find the running total of daily revenue per region.
Design a star schema for an e-commerce orders dataset — what are your fact and dimension tables?
How would you implement a slowly changing dimension (Type 2) for customer address history?

Role-specific

Design a pipeline that ingests clickstream events and makes them queryable within minutes. How do you handle duplicates and late arrivals?
A daily ETL job suddenly takes 4x longer. Walk me through how you'd diagnose and fix it.
How do you partition and store a 10TB fact table so analysts can query it efficiently?
When would you choose a columnar warehouse like Redshift over a row store, and why?

Behavioral

Tell me about a time you took ownership of a problem outside your direct responsibility.
Describe a time you dove deep into data to find the root cause of an issue others had given up on.
Tell me about a time you delivered results under a tight deadline with incomplete information.
Describe a time you disagreed with a teammate or manager on a technical decision.

Practice these problems live

Relevant LeetCode problems for the Amazon Data Engineer loop. Start a live, AI-run coding interview on any of them — or open the problem on LeetCode.

▶ Department Top Three Salaries· Hard · LeetCode #185

Department Top Three Salaries tests window functions and ranking — core Amazon DE SQL.

▶ Game Play Analysis IV· Medium · LeetCode #550

Game Play Analysis IV mirrors the retention/first-event logic Amazon loves to probe.

▶ Immediate Food Delivery II· Medium · LeetCode #1174

Immediate Food Delivery II checks conditional aggregation and date logic on event tables.

▶ Combine Two Tables· Easy · LeetCode #175

Combine Two Tables verifies clean LEFT JOIN reasoning and null handling.

▶ Rising Temperature· Easy · LeetCode #197

Rising Temperature exercises self-joins on dates, common in pipeline correctness checks.

▶ Employees Earning More Than Their Managers· Easy · LeetCode #181

Employees Earning More Than Their Managers tests self-join intuition simply and quickly.

How to answer (worked examples)

Design a pipeline for clickstream events queryable within minutes.

Structure it: ingestion (Kinesis/Kafka), processing (streaming or micro-batch), storage (partitioned by event date), and serving (warehouse/lake). Then volunteer the hard parts Amazon is listening for — idempotency for duplicate events, watermarking for late arrivals, schema evolution, and what happens when a stage fails mid-run. Tie it back to a Leadership Principle by mentioning how you'd monitor and own the pipeline's reliability.

Tell me about a time you dove deep to find a root cause.

Pure STAR, mapped to 'Dive Deep'. Situation: the symptom (bad numbers, a broken metric). Task: why it landed on you. Action: the specific investigation — the queries you ran, the data you traced upstream, the hypothesis you disproved. Result: the fix plus a quantified impact and the guardrail you added so it couldn't recur. Bar Raisers reward specificity and self-driven ownership.

What Amazon looks for

Fluent SQL — joins, aggregation, and window functions without hesitation
Sound data modeling instincts: dimensional design, normalization tradeoffs, SCDs
Pipeline thinking: idempotency, late data, partitioning, failure recovery
Leadership Principles evidence in every behavioral answer, told in STAR
Ownership and Dive Deep — you trace problems to root cause and quantify impact
Red flag: vague behavioral stories with no metrics, or hand-waving past data-quality and failure cases

FAQ

How important are the Leadership Principles for a data engineer?

Critical. Even technical rounds end with LP behavioral questions, and the Bar Raiser round is largely LP-driven. Prepare 6-8 STAR stories mapped to principles like Ownership, Dive Deep, Customer Obsession, and Deliver Results — and reuse them across rounds.

Is there a coding component beyond SQL?

SQL is the core, but expect Python or PySpark questions for data manipulation and pipeline logic, plus data modeling and system-design-style pipeline discussions. Heavy algorithm puzzles are less common than for SWE.

What is the Bar Raiser round?

A trained interviewer from outside the hiring team whose job is to keep Amazon's hiring bar consistent. They focus on Leadership Principles and have effective veto power, so treat this round as seriously as the technical ones.

What SQL level should I be at?

Comfortable with multi-table joins, GROUP BY with HAVING, subqueries/CTEs, and window functions (ROW_NUMBER, RANK, running aggregates). Practice writing them under time pressure while explaining your logic.

Will they ask about specific AWS tools?

Familiarity with Redshift, S3, Glue, EMR/Spark, and Kinesis helps and signals readiness, but they care more about whether you understand the underlying concepts — partitioning, columnar storage, streaming vs batch — than memorized service names.

Rehearse the SQL and the LP stories out loud

The Amazon loop punishes candidates who can write SQL but freeze on 'tell me about a time you took ownership.' Practice both — the data modeling whiteboard and the Bar Raiser behavioral grilling — with OfferLoop's realtime voice coach before the real loop.

Practice this interview out loud →

All Amazon interviews →Amazon Software Engineer interview →Amazon Data Analyst interview →Amazon Data Scientist interview →Amazon Machine Learning Engineer interview →

OfferLoop is an independent interview-practice tool and is not affiliated with, endorsed by, or sponsored by Amazon. All company names and trademarks are the property of their respective owners.

Interview formats vary by team, level and year, and this guide is compiled from general knowledge of publicly discussed hiring processes — treat it as preparation material, not an official description of Amazon's current process.